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How Automation and AI Are Transforming Organoid Research

The life sciences are in the midst of a crucial shift, driven by the emergence of organoid-based models and the power of automation. Organoids—three-dimensional cell cultures that mimic human tissue architecture and function—are enabling researchers to ask and answer questions that were once beyond reach. Paired with advances in automation, robotics, and artificial intelligence (AI), these models are transforming drug discovery and preclinical testing, offering a more human-relevant alternative to outdated 2D cell cultures and animal models. This revolution is reshaping the pharmaceutical industry, while also holding the potential to accelerate progress in personalized medicine.

Beyond 2D: The Rise of Organoids

For decades, preclinical research has relied on 2D cell cultures, single-cell-type 3D spheroid models, and animal models, despite their limitations in replicating human biology. Organoids, which are derived from stem cells, offer a more accurate representation of human tissues, recapitulating complex biological processes such as organ-specific functionality and cellular interactions. These miniature self-organizing biological systems are being used to model diseases, test drug efficacy and toxicity, and even explore regenerative medicine.

How tiny voids could make fusion targets more stable under powerful shockwaves

Picture two materials sandwiched together. The boundary between them may appear flat, but, in reality, it is full of tiny bumps and dents. Suddenly, the materials are hit with a shockwave. If that wave hits a bump in the material interface, it slows down. If it hits a dent, it accelerates forward. This imbalance creates fast, narrow jets of material—called the Richtmyer-Meshkov (RM) instability.

In a recent paper, published in Physical Review Letters, researchers from Lawrence Livermore National Laboratory (LLNL), Imperial College London and their collaborators used AI to optimize and 3D printing to create a target that effectively negates the RM instability.

“Our target reshapes the shockwave, in both space and time, as it travels through the material,” said first author Jergus Strucka, now at the European XFEL. “Instead of a single shock hitting the surface, we introduce voids to break it up into a sequence of smaller pressure pulses that arrive at slightly different times.”

AI model ‘reads’ protein pairs, unlocking new insights into disease and drug discovery

Researchers have developed a new artificial intelligence (AI) model that can more accurately predict how proteins interact with one another—an advancement that could accelerate drug discovery and deepen insights into diseases such as cancer.

Led by Professor Zhang Yang, Senior Principal Investigator from the Cancer Science Institute of Singapore (CSI Singapore) at the National University of Singapore, and published in Nature Communications, the study introduces a paired protein language model (PPLM) that learns from two interacting proteins simultaneously, rather than analyzing them in isolation. This marks a significant shift in how AI is applied to biology, enabling more accurate prediction of protein–protein interactions that underpin nearly all cellular processes.

World’s largest collection of Olympiad-level math problems now available to everyone

Every year, the countries competing in the International Mathematical Olympiad arrive with a booklet of their best, most original problems. Those booklets get shared among delegations, then quietly disappear. No one had ever collected them systematically, cleaned them, and made them available—not for AI researchers testing the limits of mathematical reasoning, and not for the students around the world training for these competitions largely on their own.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), King Abdullah University of Science and Technology (KAUST), and HUMAIN have now done exactly that.

MathNet is the largest high-quality dataset of proof-based math problems ever created, and it is not closed. Comprising more than 30,000 expert-authored problems and solutions spanning 47 countries, 17 languages, and 143 competitions, it is five times larger than the next biggest dataset of its kind. The work will be presented at the International Conference on Learning Representations (ICLR 2026) in Brazil later this month.

AI model accurately predicts the spread of wildfires in real time

USC researchers are developing a computational model that combines satellite data and physics-based simulations to forecast a wildfire’s path, intensity, and growth rate. If you’ve ever been evacuated from your home during a wildfire, you’ll be aware of the terrifying unpredictability of the situation. From your location on the ground—rapidly gathering a few vital belongings and attempting to identify the best route to safety—there’s no way of knowing how fast a fire is growing or which direction it’s likely to take.

That was the experience of Assad Oberai, Hughes Professor of aerospace and mechanical engineering at the USC Viterbi School of Engineering. He was evacuated from his home during the Eaton Fire in January 2025—one of the most destructive wildfires in Southern California history, burning for 24 days before full containment and leaving more than 9,400 structures destroyed and over 1,000 damaged.

“Due to changing climate, we’re seeing more of these extremely intense fires—those that burn very fast and very bright,” he reflected. “We have the data at our fingertips. It all comes down to how we put it to use.”

The Gentlemen ransomware now uses SystemBC for bot-powered attacks

A SystemBC proxy malware botnet of more than 1,570 hosts, believed to be corporate victims, has been discovered following an investigation into a Gentlemen ransomware attack carried out by a gang affiliate.

The Gentlemen ransomware-as-a-service (RaaS) operation emerged around mid-2025 and provides a Go-based locker that can encrypt Windows, Linux, NAS, and BSD systems, and a C-based locker for ESXi hypervisors.

Last December, it compromised one of Romania’s largest energy providers, the Oltenia Energy Complex. Earlier this month, The Adaptavist Group disclosed a breach that Gentlemen ransomware listed on its data leak site.

US Army Tests Autonomous Golden Shield Counter-drone System in Live-fire Exercise

The U.S. Army 1st Cavalry Division has completed the latest phase of its counter drone experimentation, a live-fire exercise from April 7–9 testing cUAS systems for its “Golden Shield” counter-drone concept for an armored formation. This significant step in the division’s Pegasus Charge initiative incorporated autonomous cUAS battlefield effectors for the first time, advancing efforts to protect U.S. forces from the growing threat of small unmanned aerial systems. Exercise Golden Shield integrated advanced sensors, kinetic and non-kinetic effectors and command-and-control systems to create an autonomous cohesive defense against small UAS. The effort, led by the 1st Cavalry Division in collaboration with Army DEVCOM and industry partners, aims to enhance the protection of armored vehicles and their crews while maneuvering. The system links sensors and weapons on tactical vehicles to automatically detect, track and engage threats, significantly shortening the sensor-to-shooter timeline and reducing cognitive load.

“The intent is to take these systems we tested this week and begin to integrate them within our armored formations’ training,” said Maj. Kevin Correa, 1st Cavalry Division’s air and missile defense chief. “In that way, we are able to fully exercise not only the systems, but the tanker’s ability to manage these systems while conducting their normal operations.”

“The future is formation-based layered protection, and this is the start of that,” said Alfred Grein, executive director for Research and Technology Integration for the U.S. Army Capabilities Development Command Ground Vehicle Systems Center. “Some (of the systems) are more mature than others. But understand that’s part of why we do experiments to determine what we think is ready to hand-off to Soldiers in the field environment.”

Sensing steroid hormone 17α-hydroxypregnenolone by GPR56 enables protection from ferroptosis-induced liver injury

Online now:(Cell Metabolism 36, 2402–2418.e1–e10; November 5, 2024)

Online now: (Cell Metabolism 36, 2402–2418.e1–e10; November 5, 2024)

In the originally published article, due to figure preparation mistakes, there were errors in Figures 2, 3, and S9. Specifically, the line legends in Figure 2J were accidentally lost during the creation of the figure using AI software, the marker positions for the β-actin bands in Figure 3J were incorrectly labeled, the H&E staining image of the wild-type mouse DOX+17-OH PREG treatment group in Figure S9A was erroneously pasted during figure compilation, and the IHC staining image of the liver ischemia-reperfusion treatment group in Figure S9I was flipped during copying. We apologize for these oversights that occurred during the many revisions.

Because certain western bands were not clear, we corrected Figures 2C and 3G with full-membrane original data. In addition, CD36 appears to be over 100 kDa in Figure S10S, whereas it is consistently between 70 and 100 kDa in all other figures. We have previously encountered similar problems with certain proteins with a little difference in molecular weight, and we have solved this issue by using other lysis buffers. Therefore, we used another lysis buffer (epizyme CAT: PC201) to examine whether there is a consistent phenotype of CD36 between 70 and 100 kDa. As expected, we detected a significant decrease of CD36 located within 70–100 kDa upon IR, Dox, and MCDD treatment, which was consistent with our published data of CD36 above 100 kDa. Because the major CD36 band should appear at approximately 88 kDa based on numerous studies, we have removed the original data from Figure S10S and presented the corrected bands in Figure S10U to avoid confusion.

Feynman: The Past and Future Are the Same Thing

The past and future are the same thing | feynman on time symmetry.

Discover one of physics’ most mind-bending secrets: the fundamental laws of nature don’t know which way time flows! In this exploration of Feynman’s ideas on time symmetry, we dive deep into how the equations of physics work equally well forwards and backwards, why positrons are electrons moving backward through time, and how the Wheeler-Feynman absorber theory suggests the future might influence the past.

From billiard balls to quantum mechanics, from Maxwell’s equations to the mystery of why we remember yesterday but not tomorrow, this video unravels the beautiful symmetry hidden beneath our everyday experience of time.

Topics Covered:
• Time symmetry in fundamental physics
• Positrons as electrons traveling backward in time
• Wheeler-Feynman absorber theory
• The thermodynamic arrow of time
• Path integral formulation and quantum mechanics
• Why time appears to flow in one direction
• CP violation and the weak nuclear force.

Perfect for physics enthusiasts, students, and anyone curious about the nature of time and reality.

⚠️ DISCLAIMER: This is AI-generated content created in the style of Richard Feynman’s teaching approach. The script synthesizes information from various sources about Feynman’s work and ideas in theoretical physics, including his lectures, published papers, and documented contributions to quantum electrodynamics and time-symmetric theories. While based on authentic concepts from Feynman’s career, this is an educational interpretation and not actual recorded material from Richard Feynman.

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